A mathematical model for peripheral nerve conduction velocity.

OBJECTIVE

To derive a mathematical model for peripheral axon geometry, and apply it to the prediction of latencies along a nerve.

DESIGN

Retrospective review of data from individuals with bilaterally normal EMG/NCS, those with a diagnosis of carpal tunnel syndrome alone, and data from previous researchers.

SETTING

Electrodiagnostic laboratory at a teaching hospital.

SUBJECTS

Twenty-two (22) individuals with bilaterally normal EMG/NCS, and 61 hands from 40 individuals with carpal tunnel syndrome. Data from previous researchers was also utilized.

RESULTS

Applying an exponentially tapering axon model to normal data yielded a formula for latency (L = kd 0.775) where k is a constant, and d is the distance from the distal end of the nerve. This formula produced a correlation of 0.777 for predicting median distal motor latencies using the proximal latency, and 0.676 for the ulnar nerve. The largest difference between predicted and actual distal latency was 0.48 msecs for the median nerve and 0.60 msecs for the ulnar nerve. This formula correctly classified as abnormal 3 (37.5%) out of 8 carpal tunnel syndrome cases with completely normal motor studies by standard criteria. This formula also agreed well with the data of other researchers, predicting normal distal latencies, F wave latencies, and identifying abnormal data.

CONCLUSIONS

A single model for axon geometry based on uniform exponential tapering accurately predicts latencies for many nerves, and can detect subtle neuropathology.